Control Channel Coding Rate Management via Machine Learning
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Solution Overview
Problem
Existing network systems face challenges in dynamically managing coding rates of control channels due to static predetermined rates, which fail to balance control channel robustness and network resource efficiency, especially under varying channel conditions and frequent changes in user access patterns.
Innovation Solution
A machine learning (ML) model is used to continuously determine an optimal coding rate for control channels based on current network data, automatically adjusting coding rates without manual intervention, thereby adapting to dynamic conditions and optimizing both robustness and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static predetermined coding rates are used, then control channel robustness is maintained, but network resource efficiency deteriorates under varying channel conditions
Solution Approach 1:
The patent applies dynamics by transitioning from static predetermined coding rates to dynamic coding rates that adapt to varying channel conditions. The system continuously monitors channel quality indicators (CQI) and adjusts coding rates in real-time, allowing the coding rate to change according to current network conditions rather than remaining fixed.
Solution Approach 2:
The patent changes the parameter of coding rate from a fixed predetermined value to a variable parameter that adjusts based on channel conditions. By modifying the coding rate parameter dynamically according to CQI measurements, the system optimizes the balance between robustness and resource efficiency for different transmission scenarios.
2Adaptability or versatility
If manual reconfiguration of coding rates is performed, then coding rate adjustments can be made, but operational efficiency deteriorates due to user intervention requirements
Solution Approach 1:
The patent implements self-service by enabling the system to automatically monitor channel conditions, determine appropriate coding rates, and reconfigure transmissions without user intervention. The network device autonomously adjusts coding rates based on received CQI feedback, eliminating the need for manual configuration while maintaining adaptability.
Solution Approach 2:
The patent uses feedback mechanisms where the system continuously receives channel quality indicators from user equipment, processes this information, and adjusts coding rates accordingly. This closed-loop feedback enables automatic adaptation to changing conditions without requiring manual reconfiguration.
3Productivity
If frequent coding rate adjustments are made, then network resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies partial action by adjusting coding rates based on actual channel conditions rather than continuously or excessively. The system monitors CQI and makes coding rate adjustments only when channel conditions warrant changes, avoiding unnecessary reconfigurations while maintaining resource efficiency.
Data Source
AI summary
Provided are apparatus, method, and device for managing control channel coding rate. The apparatus including: a memory storage storing computer-executable instructions; and at least one processor communicatively coupled to the memory storage, wherein the at least one processor is configured to execute the instructions to: obtain data relating to a current coding rate of a control channel and network data relating to a current control channel quality; analyze, by a machine learning (ML) model, the obtained data and the obtained network data to determine an optimal coding rate for the control channel; and output the determined optimal coding rate.